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A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 3, doi: 10.1007/s11465-022-0688-0

Abstract: have been considered in this study: those that perform feature extraction by using the convolutional neuralnetworks and those based on an explicit feature extraction procedure.

Keywords： precision milling     dimensional accuracy     cutting force     convolutional neural networks     coherent noise    

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Novel interpretable mechanism of neural networks based on network decoupling method

Frontiers of Engineering Management 2021, Volume 8, Issue 4,   Pages 572-581 doi: 10.1007/s42524-021-0169-x

Abstract: The lack of interpretability of the neural network algorithm has become the bottleneck of its wide applicationdecoupled dimension reduction method of high-dimensional system and reveal the calculation mechanism of the neuralthat a simple linear mapping relationship exists between network structure and network behavior in the neuralnew interpretation mechanism provides not only the potential mathematical calculation principle of neuralor animal activities, which can further expand and enrich the interpretable mechanism of artificial neural

Keywords： neural networks     interpretability     dynamical behavior     network decouple    

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Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base Research

Fan XU, Jin WANG, Guo-dong LU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1316-1327 doi: 10.1631/FITEE.1601707

Abstract: An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parametersA radial basis function neural network is adopted for all kinds of dynamical estimation, including undesiredSpecialized robust compensation is established for global stability.Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters

Keywords： Cooperative manipulators     Neural networks     Inaccurate translational base frame     Adaptive control     Robust    

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Towards robust neural networks via a global and monotonically decreasing robustness training strategy Research Article

Zhen LIANG, Taoran WU, Wanwei LIU, Bai XUE, Wenjing YANG, Ji WANG, Zhengbin PANG,liangzhen@nudt.edu.cn,wwliu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10,   Pages 1375-1389 doi: 10.1631/FITEE.2300059

Abstract: Robustness of deep neural networks (DNNs) has caused great concerns in the academic and industrial communitiesInstead of verifying whether the robustness property holds or not in certain neural networks, this paper

Keywords： Robust neural networks     Training method     Drawdown risk     Global robustness training     Monotonically decreasing    

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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Frontiers of Chemical Science and Engineering 2013, Volume 7, Issue 3,   Pages 357-365 doi: 10.1007/s11705-013-1336-3

Abstract: Several simulation systems including a back-propagation neural network (BPNN), a radial basis functionneural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested andThe performance of these networks was evaluated using the coefficient of determination ( ) and the mean

Keywords： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial NeuralNetworks (ANNs).

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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Adaptive robust beamformer formulti-pair two-way relay networks with imperfect channel state information

Jin WANG,Feng SHU,Ri-qing CHEN,Yu-di CUI,Yu CHEN,Jun LI

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 3,   Pages 265-280 doi: 10.1631/FITEE.1500134

Abstract: In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station (RS)In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximumshown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust

Keywords： Multi-pair two-way relay     Adaptive robust beamformer     Channel state information (CSI)     Maximum signal-to-interference-and-noise    

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Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Frontiers of Structural and Civil Engineering 2018, Volume 12, Issue 4,   Pages 490-503 doi: 10.1007/s11709-017-0445-3

Abstract: This paper is aimed at adapting Artificial Neural Networks (ANN) to predict the strength properties of

Keywords： artificial neural networks     root mean square error     SIFCON     silica fume     metakaolin     steel fiber    

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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 105-113 doi: 10.1007/s11708-016-0393-y

Abstract: This paper proposes the day-ahead electricity price forecasting using the artificial neural networks

Keywords： day-ahead electricity markets     price forecasting     load forecasting     artificial neural networks     load serving    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 2,   Pages 520-536 doi: 10.1007/s11709-021-0689-9

Abstract: Two artificial-intelligence-based models including artificial neural networks and support vector machinessupport vector machines in predicting the strength of the investigated soils compared with artificial neuralnetworks.

Keywords： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 2,   Pages 167-177 doi: 10.1007/s11709-014-0236-z

Abstract: Artificial neural network (ANN) approach has been also employed to derive empirical formulae for predicting

Keywords： steel I-beams     lateral-torsional buckling     finite element (FE) method     artificial neural network (ANN) approach    

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A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks

Zhang Junyan,Feng Shouzhong,Liu Donghai

Strategic Study of CAE 2005, Volume 7, Issue 10,   Pages 87-90

Abstract: inflexion points, a method for forecasting tunnel surrounding rock deformation using radial basis function neuralnetworks is presented.curves, but also has higher convergence speed and better globally-searching ability than those using BP neuralnetworks.

Keywords： RBF neural networks     tunnel construction     surrounding rock deformation     forecasting    

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Diffractive Deep Neural Networks at Visible Wavelengths Article

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Engineering 2021, Volume 7, Issue 10,   Pages 1485-1493 doi: 10.1016/j.eng.2020.07.032

Abstract: One landmark method is the diffractive deep neural network (D2NN) based on three-dimensional printing

Keywords： Optical computation     Optical neural networks     Deep learning     Optical machine learning     Diffractive deepneural networks    

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immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 2,   Pages 154-167 doi: 10.1007/s11465-015-0336-z

Abstract: The objective function of the optimizer is derived from a computationally efficient artificial neural

Keywords： information preview     intelligent transportation     state-of-charge trajectory builder     immune systems     artificial neural    

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Service life prediction of fly ash concrete using an artificial neural network

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 3,   Pages 793-805 doi: 10.1007/s11709-021-0717-9

Abstract: lifetime of fly ash concrete by developing a carbonation depth prediction model that uses an artificial neuralMoreover, experimental validation carried out for the developed model shows that the artificial neural

Keywords： concrete     fly ash     carbonation     neural networks     experimental validation     service life    

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Title Author Date Type Operation

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of

Journal Article

Novel interpretable mechanism of neural networks based on network decoupling method

Journal Article

Adaptive robust neural control of a two-manipulator system holding a rigid object with inaccurate base

Fan XU, Jin WANG, Guo-dong LU

Journal Article

Towards robust neural networks via a global and monotonically decreasing robustness training strategy

Zhen LIANG, Taoran WU, Wanwei LIU, Bai XUE, Wenjing YANG, Ji WANG, Zhengbin PANG,liangzhen@nudt.edu.cn,wwliu@nudt.edu.cn

Journal Article

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networks

J. Sargolzaei, A. Hedayati Moghaddam

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Journal Article

Adaptive robust beamformer formulti-pair two-way relay networks with imperfect channel state information

Jin WANG,Feng SHU,Ri-qing CHEN,Yu-di CUI,Yu CHEN,Jun LI

Journal Article

Predicting the strength properties of slurry infiltrated fibrous concrete using artificial neural network

T. Chandra Sekhara REDDY

Journal Article

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Journal Article

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and support

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

Journal Article

Lateral-torsional buckling capacity assessment of web opening steel girders by artificial neural networks

Yasser SHARIFI,Sajjad TOHIDI

Journal Article

A Forecasting Method for Tunnel Surrounding Rock Deformation Using RBF Neural Networks

Zhang Junyan,Feng Shouzhong,Liu Donghai

Journal Article

Diffractive Deep Neural Networks at Visible Wavelengths

Hang Chen, Jianan Feng, Minwei Jiang, Yiqun Wang, Jie Lin, Jiubin Tan, Peng Jin

Journal Article

immune-inspired optimum state-of-charge trajectory estimation using upcoming route information preview and neuralnetworks for plug-in hybrid electric vehicles fuel economy

Ahmad MOZAFFARI,Mahyar VAJEDI,Nasser L. AZAD

Journal Article

Service life prediction of fly ash concrete using an artificial neural network

Journal Article